yolov5 資料增強代碼
主要有以下幾種方式：
class Albumentations # 資料增強package,比pytorch 自帶的transform 更豐富
def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5) # 影像增強方式，hgain 是色調，不同色調不同顏色，sgain是飽和度， vgain是亮度
def hist_equalize(im, clahe=True, bgr=False):# 采用自適應直方圖均衡化做影像增強
def replicate(im, labels) #
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32) # 影像size擴充至指定大小
def random_perspective(im, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
border=(0, 0)) # 隨機增強
def copy_paste(im, labels, segments, p=0.5)# 復制粘貼
def cutout(im, labels, p=0.5) # 裁剪
def mixup(im, labels, im2, labels2) # mixup
def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16) # 框篩選
下面一個個來看影像增強的方式：
Albumentations 影像增強
class Albumentations:
# YOLOv5 Albumentations class (optional, only used if package is installed)
def __init__(self):
self.transform = None
try:
'''
albumentations --一個資料增強的package,比pytorch的transform豐富；詳情
https://blog.csdn.net/cp1314971/article/details/106039800?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522164015856916780261966386%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=164015856916780261966386&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~baidu_landing_v2~default-1-106039800.pc_search_es_clickV2&utm_term=import+albumentations+&spm=1018.2226.3001.4187
'''
import albumentations as A
check_version(A.__version__, '1.0.3') # version requirement
self.transform = A.Compose([
A.Blur(p=0.01), # 影像隨機大小內核模糊輸入影像
A.MedianBlur(p=0.01), # 影像隨機模糊輸入影像
A.ToGray(p=0.01), # 轉成灰度圖
A.CLAHE(p=0.01), #
A.RandomBrightnessContrast(p=0.0), # 隨機亮度和對比度
A.RandomGamma(p=0.0), #
A.ImageCompression(quality_lower=75, p=0.0)], # 影像壓縮
bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) #
logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))
except ImportError: # package not installed, skip
pass
except Exception as e:
logging.info(colorstr('albumentations: ') + f'{e}')
def __call__(self, im, labels, p=1.0):
if self.transform and random.random() < p:
new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed
im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
return im, labels
hsv 色調-飽和度-亮度的影像增強
def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): # 做h-色調， s-飽和度， v-亮度上面的隨機增強
# HSV color-space augmentation
if hgain or sgain or vgain:
r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains 生成3個[-1, 1)之間的亂數，分別與hsv相乘后+1 [0,2]之間
hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) # 將影像從BGR 轉成HSV ，拆分
dtype = im.dtype # uint8
x = np.arange(0, 256, dtype=r.dtype) # [0, 1, 2,...,255]
lut_hue = ((x * r[0]) % 180).astype(dtype) # [0, 180)
lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) # 將陣列截斷至[0, 255]
lut_val = np.clip(x * r[2], 0, 255).astype(dtype) # 將陣列截斷至[0, 255]
im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
# cv2.LUT lookup-table 查找表方式，即通過lut_hue 這個表對之前hue數值做修正，回傳0-255對應位置的lut_hue值 具體： https://blog.csdn.net/Dontla/article/details/103963085
# cv2.merge 合并三個通道
cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed
直方圖均衡化增強
def hist_equalize(im, clahe=True, bgr=False): # 直方圖均衡化增強 參考 https://www.cnblogs.com/my-love-is-python/p/10405811.html
# Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255
yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) # 將影像從bgr轉成YUV
if clahe:
c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
# cv2.createCLAHE 實體化自適應直方圖均衡化函式 區域直方圖均衡化 ，不會使得細節消失
# c.apply 進行自適應直方圖均衡化
yuv[:, :, 0] = c.apply(yuv[:, :, 0])
else:
# cv2.equalizeHist 進行像素點的均衡化 ，即全域均衡化 ，使得整體亮度提升，但是區域會模糊
yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram
return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB
影像框的平移復制增強
def replicate(im, labels): # 復制，實際上指的是框的平移
# Replicate labels
h, w = im.shape[:2] # 獲取影像長寬
boxes = labels[:, 1:].astype(int) # 獲取框的位置和大小
x1, y1, x2, y2 = boxes.T # 框的左右和上下位置
s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels)
for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices
x1b, y1b, x2b, y2b = boxes[i]
bh, bw = y2b - y1b, x2b - x1b
yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y
x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax]
labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
return im, labels
影像以letterbox縮放
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
# 按比例縮放圖片，并將其他部分填充，到resize圖片的大小
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int): # 如果輸入是一個數字，默認長寬相等
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) #
if not scaleup: # only scale down, do not scale up (for better val mAP) # 如果只縮小，不放大圖片
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) # 對圖片按比例縮放后的長寬 (width, height)
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding 對縮放后的影像 需要填充的size
if auto: # minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding 取能被stride 整除的dw 和dh
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) # 先將圖片按比例縮放到指定大小
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) # 上下位置
left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) # 左右位置
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
# cv2.copyMakeBorder 對im設定邊界框
return im, ratio, (dw, dh)
旋轉等變換（未更新完全，后續補充）
def random_perspective(im, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
border=(0, 0)):
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
# targets = [cls, xyxy]
height = im.shape[0] + border[0] * 2 # shape(h,w,c)
width = im.shape[1] + border[1] * 2
# Center [w, h, c] -->[w/2 , h/2, c]
'''
[ 1 0 -w/2
0 1 -h/2
0 0 1
]
'''
C = np.eye(3)
C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
# Perspective [w, h, c] -->[w/2 , h/2, c]
'''
[ 1 0 0
rand 1 0
rand 0 1
]
'''
P = np.eye(3)
P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
# Rotation and Scale
R = np.eye(3)
a = random.uniform(-degrees, degrees)
# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
s = random.uniform(1 - scale, 1 + scale)
# s = 2 ** random.uniform(-scale, scale)
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
# Shear
S = np.eye(3)
S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
# Translation
T = np.eye(3)
T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)
T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)
# Combined rotation matrix
M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT tf.matmul(A,C)=np.dot(A,C)= A@C
if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
if perspective:
im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
else: # affine
im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) # cv2.warpAffine()放射變換函式，可實作旋轉，平移，縮放；變換后的平行線依舊平行
# cv2.warpAffine()放射變換函式，可實作旋轉，平移，縮放；變換后的平行線依舊平行
# cv2.warpPerspective()透視變換函式，可保持直線不變形，但是平行線可能不再平行
# Visualize
# import matplotlib.pyplot as plt
# ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
# ax[0].imshow(im[:, :, ::-1]) # base
# ax[1].imshow(im2[:, :, ::-1]) # warped
# Transform label coordinates
n = len(targets)
if n:
use_segments = any(x.any() for x in segments)
new = np.zeros((n, 4))
if use_segments: # warp segments
segments = resample_segments(segments) # upsample
for i, segment in enumerate(segments):
xy = np.ones((len(segment), 3))
xy[:, :2] = segment
xy = xy @ M.T # transform
xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine
# clip
new[i] = segment2box(xy, width, height)
else: # warp boxes
xy = np.ones((n * 4, 3))
xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
xy = xy @ M.T # transform
xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
# create new boxes
x = xy[:, [0, 2, 4, 6]]
y = xy[:, [1, 3, 5, 7]]
new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
# clip
new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
# filter candidates
i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
targets = targets[i]
targets[:, 1:5] = new[i]
return im, targets
復制粘貼
def copy_paste(im, labels, segments, p=0.5):
# Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
n = len(segments)
if p and n:
h, w, c = im.shape # height, width, channels
im_new = np.zeros(im.shape, np.uint8)
for j in random.sample(range(n), k=round(p * n)):
l, s = labels[j], segments[j]
box = w - l[3], l[2], w - l[1], l[4]
ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
if (ioa < 0.30).all(): # allow 30% obscuration of existing labels
labels = np.concatenate((labels, [[l[0], *box]]), 0)
segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
result = cv2.bitwise_and(src1=im, src2=im_new)
result = cv2.flip(result, 1) # augment segments (flip left-right)
i = result > 0 # pixels to replace
# i[:, :] = result.max(2).reshape(h, w, 1) # act over ch
im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug
return im, labels, segments
圖片隨機馬賽克
def cutout(im, labels, p=0.5):# 隨機馬賽克
# Applies image cutout augmentation https://arxiv.org/abs/1708.04552
if random.random() < p:
h, w = im.shape[:2]
scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16
# [0.5 0.25 0.25 0.125 0.125 0.0125 ... 0.03125] 31
# image size fraction
for s in scales:
mask_h = random.randint(1, int(h * s)) # create random masks
mask_w = random.randint(1, int(w * s))
# box 確定隨機馬賽克的位置
xmin = max(0, random.randint(0, w) - mask_w // 2)
ymin = max(0, random.randint(0, h) - mask_h // 2)
xmax = min(w, xmin + mask_w)
ymax = min(h, ymin + mask_h)
# apply random color mask 確定隨機馬賽克的馬賽克像素
im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] #
# return unobscured labels
if len(labels) and s > 0.03: #
box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area 計算馬賽克位置與框位置的IOU 大小
labels = labels[ioa < 0.60] # remove >60% obscured labels # 保留下與馬賽克框iou小于 0.6的，大于0.6的認為沒有學習的必須
return labels
mixup
def mixup(im, labels, im2, labels2): # 兩張圖片像素點以不同權重融合
# Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
im = (im * r + im2 * (1 - r)).astype(np.uint8) # img 做融合
labels = np.concatenate((labels, labels2), 0)
return im, labels
過濾影像
def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
# 過濾掉長寬小于閾值，面積圖片占比小于閾值，框長寬比大于閾值的框
# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
# box1 圖片 ，box2 框
w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio 寬長比與長寬比的最大值
return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
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